Quantifying QB Throw Decision-Making in Football

Zachary Pipping, Lou Zhou | Karim Kassam

Motivating Example

Motivating Questions

How likely would other QBs make that same decision?

Can we quantify how reckless or conservative a QB is?
- Are QBs being too reckless or too conservative in their throws?

Contextualizing touchdown throws / interceptions
- Would other QBs make the same decision?

Look to build a ranking model which determines the most likely receiver at a frame given throw attempt

Data Overview

2025 NFL Big Data Bowl – Weeks 1–9

Game and Play Data – Teams, Score, Play Description, Game Context, Play Result, Changes in Win Probability

Player Play Data – Statistics for each player for a play

Tracking Data - Locations of players and the football at each frame of a play

Exclusively looking at throwing plays with an obvious target
- Removing spikes and throwaways

Current Spacing Tells an Incomplete Story

Speed and Orientation as a Proxy for Future Separation

Methodology

Building a ranking algorithm(i.e. XGBoost) to rank the likeliest recipient at a frame - 53.0 \(\pm\) 0.6% accuracy
- Imputing Features like distance from tracking and event data
- Previous Work: Deep Learning Approach¹, 59.8% accuracy

Applying model to quantify recklessness / conservative tendencies of quarterbacks

Feature Set

Recipient Features - Distances, Differences in Orientations and Speeds from Top 5 Closest Defenders, Whether the Throw will Result in a First Down, Space Creation¹, Receiver Skill

Quarterback Features - Distance from Receiver, Movement Vector, Defensive Pressure

Game Context - Win Probability, Quarter, Down and Distance, Score Differential

Future Steps

Continuing to build feature set to increase model performance
- Looking to perform comparably with previous deep learning approach

Cutting down on Redundant Features and Parameter Tuning
- Building the simplest model with the strongest predictive power

Quantifying Recklessness with Model Outputs

Appendix